From Natural Language to SQL with AI: Building an Intelligent SQL Query Generator Using Hugging Face and Streamlit

A developer built a Text-to-SQL application using Hugging Face Transformers and Streamlit that converts natural language questions into SQL queries. The system uses a T5-based model to generate SQL from plain English, executes it against a SQLite database, and displays results via a Streamlit dashboard. The project demonstrates how AI can enable non-technical users to query databases without knowing SQL syntax.

Introduction Writing SQL queries is a fundamental skill for developers, data analysts, and database administrators. However, not everyone knows SQL syntax, and even experienced developers spend time writing repetitive queries. Recent advances in Generative AI and Large Language Models LLMs make it possible to convert plain English into SQL automatically. Instead of writing: SELECT name, salary FROM employees WHERE department = 'IT' ORDER BY salary DESC; A user can simply ask: "Show me all IT employees ordered by salary from highest to lowest." The AI translates the request into SQL. In this article, we'll build a Text-to-SQL application using: Python Streamlit Hugging Face Transformers SQLite SQLAlchemy We'll also discuss real-world applications, limitations, and best practices. Why Text-to-SQL Matters Organizations generate massive amounts of structured data stored in relational databases. Business users often need answers without knowing SQL. Examples include: Sales managers checking monthly revenue HR departments analyzing employee records Finance teams generating reports Customer support searching order history AI enables these users to retrieve information using natural language. Project Architecture User Question │ ▼ Hugging Face Model │ ▼ Generated SQL Query │ ▼ SQLite Database │ ▼ Results │ ▼ Streamlit Dashboard The workflow is simple: User enters a question. AI generates SQL. SQL executes against SQLite. Results appear instantly. Technologies Used Technology Purpose Python Backend Streamlit Web Interface Hugging Face LLM for Text-to-SQL SQLAlchemy Database connection SQLite Sample database Installing Dependencies pip install streamlit pip install transformers pip install torch pip install sqlalchemy pip install pandas Creating a Sample Database from sqlalchemy import create engine engine = create engine "sqlite:///company.db" engine.execute """ CREATE TABLE employees id INTEGER PRIMARY KEY, name TEXT, department TEXT, salary INTEGER """ Insert sample data: engine.execute """ INSERT INTO employees name, department, salary VALUES 'Alice','IT',7500 , 'Bob','Sales',5200 , 'Carol','IT',8900 """ Loading a Hugging Face Model One popular Text-to-SQL model is based on T5. from transformers import AutoTokenizer, AutoModelForSeq2SeqLM model name = "tscholak/1wnr382e" tokenizer = AutoTokenizer.from pretrained model name model = AutoModelForSeq2SeqLM.from pretrained model name Converting Natural Language into SQL question = "Show employees working in IT" inputs = tokenizer question, return tensors="pt" outputs = model.generate inputs sql = tokenizer.decode outputs 0 , skip special tokens=True print sql Possible output: SELECT FROM employees WHERE department='IT'; Executing the SQL import pandas as pd result = pd.read sql sql, engine print result Output: id name department salary 1 Alice IT 7500 3 Carol IT 8900 Building the Streamlit Interface import streamlit as st question = st.text input "Ask your database" if st.button "Generate SQL" : sql = generate sql question st.code sql, language="sql" result = pd.read sql sql, engine st.dataframe result Now users only need to type questions such as: Show all employees List employees in Sales Average salary by department Highest paid employee Real-World Applications Business Intelligence Employees can generate reports without learning SQL. Healthcare Doctors can retrieve patient records using natural language. Banking Analysts can summarize transactions through conversational queries. E-commerce Managers can ask: "Which products sold the most last month?" instead of writing complex SQL. Challenges Although Text-to-SQL is impressive, it has limitations. Database Schema Understanding The AI performs much better when it understands the database schema. SQL Validation Generated SQL should always be validated before execution. Never execute AI-generated SQL directly in production. Security Restrict permissions to read-only whenever possible. Avoid allowing AI to execute: DELETE UPDATE DROP ALTER without human approval. Best Practices ✅ Provide the database schema as context. ✅ Validate SQL syntax. ✅ Limit user permissions. ✅ Log generated queries. ✅ Review queries before execution. Public GitHub Example A complete open-source implementation can be found in projects like: https://github.com/vanna-ai/vanna https://github.com/vanna-ai/vanna https://github.com/defog-ai/sqlcoder https://github.com/defog-ai/sqlcoder https://github.com/langchain-ai/langchain https://github.com/langchain-ai/langchain SQL agents These repositories demonstrate production-ready approaches for natural language querying over SQL databases. Future Improvements Some ideas to extend this project include: PostgreSQL support MySQL support SQL Server support Query explanation Chart generation Conversational memory Retrieval-Augmented Generation RAG Integration with local LLMs using Ollama Conclusion Text-to-SQL is transforming how users interact with relational databases. By combining Hugging Face, Streamlit, and Python, developers can build applications that allow anyone to query databases using natural language. While these systems require careful validation and security controls, they significantly lower the barrier to accessing structured data and improve productivity for both technical and non-technical users. As open-source AI models continue to improve, conversational database interfaces will become an increasingly common feature in modern data applications. References Hugging Face Transformers: https://huggingface.co/docs/transformers https://huggingface.co/docs/transformers Streamlit Documentation: https://streamlit.io https://streamlit.io SQLAlchemy Documentation: https://docs.sqlalchemy.org https://docs.sqlalchemy.org Vanna AI: https://github.com/vanna-ai/vanna https://github.com/vanna-ai/vanna SQLCoder: https://github.com/defog-ai/sqlcoder https://github.com/defog-ai/sqlcoder